feat: Initial CBPOA commit — 武汉儿童呼吸疾病风险评估系统
Context: Build a spatial risk assessment system correlating air quality data with children's respiratory disease incidence across Wuhan. Approach: FastAPI backend serving PostGIS spatial queries, React frontend with Deck.gl maps, and a PyTorch SpatialTemporalGCN pipeline for multi-day (1d/3d/7d) risk prediction. Changes: - backend/ — FastAPI API with auth (JWT), alerts, risk analysis, geocoded case data, grid statistics, and report endpoints - frontend/ — React dashboard with interactive risk maps, alert monitoring, district comparison charts, and timeline player - models/ — SpatialTemporalGCN model with trained weights and ONNX export for inference - scripts/ — ETL pipeline for weather + medical data, grid generation, feature engineering, training, and daily inference - deploy/ — Docker Compose configs for backend, frontend, and MLflow - docs/ — API docs, deployment guide, user guide, and code review Impact: Enables spatial risk visualization, alert monitoring, and ML-driven health risk forecasting for environmental health teams.
This commit is contained in:
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scripts/deploy_schema.sql
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277
scripts/deploy_schema.sql
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-- Wuhan Children's Respiratory Disease Risk Prediction - PostGIS Schema
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-- Database: wuhan_risk
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-- Created: 2026-04-25
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-- Enable PostGIS extension
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CREATE EXTENSION IF NOT EXISTS postgis;
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CREATE EXTENSION IF NOT EXISTS postgis_topology;
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-- Drop existing tables if they exist (for re-deployment)
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DROP TABLE IF EXISTS alerts CASCADE;
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DROP TABLE IF EXISTS risk_predictions CASCADE;
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DROP TABLE IF EXISTS medical_daily CASCADE;
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DROP TABLE IF EXISTS weather_daily CASCADE;
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DROP TABLE IF EXISTS road_edges CASCADE;
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DROP TABLE IF EXISTS road_nodes CASCADE;
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DROP TABLE IF EXISTS wuhan_districts CASCADE;
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-- ============================================================================
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-- Table: wuhan_districts
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-- Description: Wuhan administrative district boundaries
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-- Source: Datas/武汉市.geojson
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-- ============================================================================
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CREATE TABLE wuhan_districts (
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district_code VARCHAR(6) PRIMARY KEY,
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district_name VARCHAR(100) NOT NULL,
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adcode VARCHAR(6) NOT NULL,
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geom GEOMETRY(MultiPolygon, 4326) NOT NULL,
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area_km2 NUMERIC(10, 2),
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
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);
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-- Spatial index on district boundaries
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CREATE INDEX idx_wuhan_districts_geom ON wuhan_districts USING GIST (geom);
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-- ============================================================================
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-- Table: road_nodes
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-- Description: Road network nodes (intersections + segment midpoints)
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-- Source: OSM Hubei extract, filtered to Wuhan boundary
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-- ============================================================================
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CREATE TABLE road_nodes (
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osmid BIGINT PRIMARY KEY,
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node_type VARCHAR(20) NOT NULL CHECK (node_type IN ('intersection', 'midpoint')),
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lat NUMERIC(10, 8) NOT NULL,
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lon NUMERIC(11, 8) NOT NULL,
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elevation_m NUMERIC(8, 2),
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pop_density NUMERIC(10, 2),
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district_code VARCHAR(6),
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highway_tag VARCHAR(50),
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node_degree INTEGER DEFAULT 0,
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geom GEOMETRY(Point, 4326) NOT NULL,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
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);
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-- Spatial index on road nodes
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CREATE INDEX idx_road_nodes_geom ON road_nodes USING GIST (geom);
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CREATE INDEX idx_road_nodes_district ON road_nodes (district_code);
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-- ============================================================================
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-- Table: road_edges
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-- Description: Road network edges (road segments between nodes)
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-- Source: OSM Hubei extract
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-- ============================================================================
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CREATE TABLE road_edges (
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edge_id BIGINT PRIMARY KEY,
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source_osmid BIGINT NOT NULL REFERENCES road_nodes(osmid),
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target_osmid BIGINT NOT NULL REFERENCES road_nodes(osmid),
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road_type VARCHAR(50) NOT NULL,
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road_type_abbrev VARCHAR(10),
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length_m NUMERIC(10, 2) NOT NULL,
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speed_limit_kmh INTEGER,
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weight NUMERIC(10, 6) NOT NULL,
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geometry GEOMETRY(LineString, 4326) NOT NULL,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
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);
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-- Spatial index on road edges
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CREATE INDEX idx_road_edges_geometry ON road_edges USING GIST (geometry);
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CREATE INDEX idx_road_edges_source ON road_edges (source_osmid);
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CREATE INDEX idx_road_edges_target ON road_edges (target_osmid);
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-- ============================================================================
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-- Table: weather_daily
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-- Description: Daily aggregated weather and air quality data per station
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-- Source: Datas/气象 + 空气/站点_YYYYMMDD-YYYYMMDD/*.csv
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-- ============================================================================
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CREATE TABLE weather_daily (
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id BIGSERIAL PRIMARY KEY,
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date DATE NOT NULL,
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station_id VARCHAR(10) NOT NULL,
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district_code VARCHAR(6),
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lat NUMERIC(10, 8),
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lon NUMERIC(11, 8),
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aqi NUMERIC(6, 2),
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pm25 NUMERIC(8, 2),
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pm10 NUMERIC(8, 2),
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so2 NUMERIC(8, 2),
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no2 NUMERIC(8, 2),
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o3 NUMERIC(8, 2),
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co NUMERIC(8, 2),
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nox NUMERIC(8, 2),
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so2_24h NUMERIC(8, 2),
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no2_24h NUMERIC(8, 2),
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o3_8h NUMERIC(8, 2),
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co_24h NUMERIC(8, 2),
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pm10_24h NUMERIC(8, 2),
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pm25_24h NUMERIC(8, 2),
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primary_pollutant VARCHAR(50),
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air_quality_level VARCHAR(20),
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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UNIQUE(date, station_id)
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);
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-- Indexes for efficient querying
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CREATE INDEX idx_weather_daily_date ON weather_daily (date);
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CREATE INDEX idx_weather_daily_station ON weather_daily (station_id);
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CREATE INDEX idx_weather_daily_district ON weather_daily (district_code);
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CREATE INDEX idx_weather_daily_date_station ON weather_daily (date, station_id);
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-- ============================================================================
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-- Table: medical_daily
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-- Description: Daily aggregated medical visits per district
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-- Source: Datas/view_门诊.xlsx, Datas/view_住院.xlsx
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-- ============================================================================
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CREATE TABLE medical_daily (
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id BIGSERIAL PRIMARY KEY,
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date DATE NOT NULL,
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district_code VARCHAR(6) NOT NULL,
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outpatient_count INTEGER NOT NULL DEFAULT 0,
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inpatient_count INTEGER NOT NULL DEFAULT 0,
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respiratory_outpatient INTEGER NOT NULL DEFAULT 0,
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respiratory_inpatient INTEGER NOT NULL DEFAULT 0,
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total_visits INTEGER GENERATED ALWAYS AS (outpatient_count + inpatient_count) STORED,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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UNIQUE(date, district_code)
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);
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-- Indexes for efficient querying
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CREATE INDEX idx_medical_daily_date ON medical_daily (date);
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CREATE INDEX idx_medical_daily_district ON medical_daily (district_code);
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CREATE INDEX idx_medical_daily_date_district ON medical_daily (date, district_code);
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-- ============================================================================
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-- Table: risk_predictions
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-- Description: Model predictions for disease risk per road node
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-- Source: Model inference output
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-- ============================================================================
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CREATE TABLE risk_predictions (
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id BIGSERIAL PRIMARY KEY,
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date DATE NOT NULL,
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osmid BIGINT NOT NULL REFERENCES road_nodes(osmid),
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district_code VARCHAR(6) NOT NULL,
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risk_1d NUMERIC(5, 4) NOT NULL CHECK (risk_1d >= 0 AND risk_1d <= 1),
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risk_3d NUMERIC(5, 4) NOT NULL CHECK (risk_3d >= 0 AND risk_3d <= 1),
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risk_7d NUMERIC(5, 4) NOT NULL CHECK (risk_7d >= 0 AND risk_7d <= 1),
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risk_level VARCHAR(10) NOT NULL CHECK (risk_level IN ('green', 'yellow', 'orange', 'red')),
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lat NUMERIC(10, 8) NOT NULL,
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lon NUMERIC(11, 8) NOT NULL,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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UNIQUE(date, osmid)
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);
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-- Indexes for efficient querying
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CREATE INDEX idx_risk_predictions_date ON risk_predictions (date);
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CREATE INDEX idx_risk_predictions_osmid ON risk_predictions (osmid);
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CREATE INDEX idx_risk_predictions_district ON risk_predictions (district_code);
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CREATE INDEX idx_risk_predictions_level ON risk_predictions (risk_level);
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CREATE INDEX idx_risk_predictions_date_district ON risk_predictions (date, district_code);
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-- ============================================================================
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-- Table: alerts
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-- Description: Generated alerts based on risk predictions and medical data
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-- Source: Alert engine
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-- ============================================================================
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CREATE TABLE alerts (
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alert_id BIGSERIAL PRIMARY KEY,
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alert_type VARCHAR(20) NOT NULL CHECK (alert_type IN ('monitoring', 'warning')),
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alert_level VARCHAR(10) NOT NULL CHECK (alert_level IN ('yellow', 'orange', 'red')),
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date DATE NOT NULL,
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district_code VARCHAR(6) NOT NULL,
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osmid BIGINT REFERENCES road_nodes(osmid),
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trigger_source VARCHAR(50) NOT NULL,
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trigger_value NUMERIC(10, 4),
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threshold NUMERIC(10, 4),
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description TEXT,
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acknowledged BOOLEAN DEFAULT FALSE,
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acknowledged_at TIMESTAMP,
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acknowledged_by VARCHAR(100),
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
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);
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-- Indexes for efficient querying
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CREATE INDEX idx_alerts_date ON alerts (date);
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CREATE INDEX idx_alerts_district ON alerts (district_code);
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CREATE INDEX idx_alerts_level ON alerts (alert_level);
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CREATE INDEX idx_alerts_type ON alerts (alert_type);
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CREATE INDEX idx_alerts_acknowledged ON alerts (acknowledged);
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CREATE INDEX idx_alerts_date_district ON alerts (date, district_code);
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-- ============================================================================
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-- Comments for documentation
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-- ============================================================================
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COMMENT ON TABLE wuhan_districts IS 'Wuhan administrative district boundaries from GeoJSON';
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COMMENT ON TABLE road_nodes IS 'Road network nodes (intersections and segment midpoints) from OSM';
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COMMENT ON TABLE road_edges IS 'Road network edges with weights for graph traversal';
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COMMENT ON TABLE weather_daily IS 'Daily aggregated weather and air quality data per monitoring station';
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COMMENT ON TABLE medical_daily IS 'Daily aggregated outpatient and inpatient counts per district';
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COMMENT ON TABLE risk_predictions IS 'GCN+Transformer model predictions for 1/3/7 day disease risk';
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COMMENT ON TABLE alerts IS 'Generated alerts from monitoring (medical) and warning (risk prediction) systems';
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COMMENT ON COLUMN road_nodes.node_type IS 'intersection: OSM node where roads meet; midpoint: center point of road segment';
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COMMENT ON COLUMN road_edges.weight IS 'Edge weight: 1/length_km for road segments, 60/speed_limit for highways';
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COMMENT ON COLUMN weather_daily.station_id IS 'Monitoring station ID (e.g., 1001A, 1002A)';
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COMMENT ON COLUMN risk_predictions.risk_level IS 'Risk level: green (<0.3), yellow (0.3-0.5), orange (0.5-0.7), red (>0.7)';
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COMMENT ON COLUMN alerts.alert_type IS 'monitoring: triggered by medical data z-scores; warning: triggered by risk predictions';
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COMMENT ON COLUMN alerts.trigger_source IS 'Source of alert trigger (e.g., outpatient_z, inpatient_z, risk_3d, risk_7d)';
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-- ============================================================================
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-- Load Wuhan districts from GeoJSON (requires ogr2ogr or manual import)
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-- Alternative: Use COPY command with pre-processed CSV
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-- ============================================================================
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-- Example: Import districts (run after processing GeoJSON to CSV)
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-- COPY wuhan_districts (district_code, district_name, adcode, geom)
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-- FROM '/path/to/wuhan_districts.csv' WITH (FORMAT csv, HEADER true);
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-- ============================================================================
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-- Helper Views
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-- ============================================================================
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-- View: Latest risk predictions per node
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CREATE OR REPLACE VIEW v_latest_risk AS
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SELECT rp.*
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FROM risk_predictions rp
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INNER JOIN (
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SELECT osmid, MAX(date) as max_date
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FROM risk_predictions
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GROUP BY osmid
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) latest ON rp.osmid = latest.osmid AND rp.date = latest.max_date;
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-- View: Active alerts (unacknowledged)
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CREATE OR REPLACE VIEW v_active_alerts AS
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SELECT *
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FROM alerts
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WHERE acknowledged = FALSE
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ORDER BY
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CASE alert_level
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WHEN 'red' THEN 1
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WHEN 'orange' THEN 2
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WHEN 'yellow' THEN 3
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END,
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date DESC;
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-- View: District-level risk summary
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CREATE OR REPLACE VIEW v_district_risk_summary AS
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SELECT
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date,
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district_code,
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COUNT(*) as node_count,
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AVG(risk_1d) as avg_risk_1d,
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AVG(risk_3d) as avg_risk_3d,
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AVG(risk_7d) as avg_risk_7d,
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SUM(CASE WHEN risk_level = 'green' THEN 1 ELSE 0 END) as green_count,
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SUM(CASE WHEN risk_level = 'yellow' THEN 1 ELSE 0 END) as yellow_count,
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SUM(CASE WHEN risk_level = 'orange' THEN 1 ELSE 0 END) as orange_count,
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SUM(CASE WHEN risk_level = 'red' THEN 1 ELSE 0 END) as red_count
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FROM risk_predictions
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GROUP BY date, district_code;
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-- ============================================================================
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-- Grant permissions (adjust as needed)
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-- ============================================================================
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-- GRANT SELECT ON ALL TABLES IN SCHEMA public TO readonly_user;
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-- GRANT SELECT, INSERT, UPDATE ON ALL TABLES IN SCHEMA public TO app_user;
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-- GRANT ALL ON ALL SEQUENCES IN SCHEMA public TO app_user;
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-- ============================================================================
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-- Schema deployment complete
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-- ============================================================================
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